Life in Pharma: Tables, Tables Everywhere

Case Study: junco Clinical Trial Analysis Package

Challenges in Pharmaceutical Programming

• The pharmaceutical industry faces unique challenges in clinical and statistical programming:

• Implementing patterns used across many table shells (developed by dedicated teams)

• Company-specific statistical methods that need to be standardized

• Complex table structures that must be consistent across studies

• Need for a core framework that ensures all company shells can be created consistently

Shells Are Table Specific, Table Creation Is Not

The Junco Package Approach

Our solution was to develop our own business logic framework for J&J table creation

Getting to Production

The Junco package provides key features needed for production-ready clinical tables:

• True-type font support: Word wrapping, pagination, and RTF export
• Higher order column counts: Utilities for spanning column headers
• Guaranteed pathability in row space
• Nearest-value (SAS-like) rounding support: Maintaining consistency with existing processes
• Statistical calculations: In accord with business logic
• Robust approach for risk diff columns

Limitations of Script-Based Solutions

• Poor portability: Hardcoded paths and environment-specific settings
• Code duplication: Functions often copied across multiple scripts
• Reload overhead: Need to source entire scripts even when only one function is needed
• No tools for documentation, testing and difficult version tracking

Creating the package

From Scripts to Standardized Tools

Code Design and API for Users

Documentation

Using roxygen2 and pkgdown

Documentation is critical for package adoption and proper use:

#' Calculate risk difference with confidence intervals
#'
#' @param group1 Vector of outcomes for first group
#' @param group2 Vector of outcomes for second group
#' @param conf.level Confidence level (default: 0.95)
#' @return A list with risk difference and Cis
#' @export
risk_diff <- function(group1, group2, conf.level = 0.95) {
 # Function implementation
}

usethis::use_pkgdown()
pkgdown::build_site()

Ensuring Package Quality

Unit Testing and Validation

Quality assurance is essential for pharmaceutical applications:

# Basic unit testing example
add_values <- function(x, y) {
  return(x + y)
}

library(testthat)

test_that("1: addition works", {
  expect_equal(add_values(2, 2), 4)
})

Test passed 😀

test_that("2: we do expected an error", {
   expect_error(add_values("a", 1))
})

Test passed 🥳

Continuous Integration and Deployment

Automating Quality Checks

CI/CD reduces manual work while improving quality:

• Automated testing: Run tests on every code change
• Code coverage: Ensure comprehensive test coverage
• Style checking: Maintain consistent coding standards
• Documentation building: Keep documentation in sync with code

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